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Work-related generative artificial intelligence adoption and employee-perceived organisational performance in Thailand's manufacturing industry
(2026-10-01)
Yudhistyra, Wecka Imam
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Srinuan, Chalita
Generative Artificial Intelligence (GenAI) is increasingly integrated into organisational work processes, yet its implications for manufacturing firms cannot be inferred from adoption intention or usage frequency alone. In contrast to deterministic workplace technologies, GenAI produces probabilistic and context-responsive outputs whose organisational value depends on employees' ability to evaluate, interpret, and apply generated content effectively. This research examines how work-related GenAI use is associated with employee perceptions of GenAI-enabled organisational performance in Thailand's manufacturing sector. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, the Human-Centred Design paradigm, and Knowledge Management theory, the research develops a framework in which perceived usability, knowledge acquisition, knowledge application, and performance expectancy influence behavioural intention, which subsequently affects use behaviour and employee-perceived GenAI-enabled organisational performance. Survey data from 381 manufacturing employees with prior GenAI experience were analysed using Partial Least Squares Structural Equation Modelling. Bayesian Network (BN) algorithms were subsequently employed to refine the theoretical structure by identifying conditional dependencies beyond the hypothesised model, while Artificial Neural Networks and Importance-Performance Map Analysis provided complementary predictive and managerial insights. The results support all hypothesised relationships. The BN-refined model further identifies direct links between knowledge application, performance expectancy, and organisational performance, suggesting that, in addition to adoption and use, GenAI-derived value also depends on employees' capacity to translate AI-generated knowledge into workplace outcomes. The findings advance technology adoption research by developing and validating a GenAI-specific extension of the UTAUT that integrates human-centred design and knowledge management perspectives. The research further demonstrates that organisational value is realised through a sequential pathway linking adoption antecedents, behavioural intention, and use behaviour, while providing policy-relevant evidence on workforce-driven AI adoption in an emerging economy undergoing digital transformation.
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Impact of human resource training on supply chain efficiency in Guiyang’s enterprises: a structural equation modelling (SEM) analysis
(2026-03-01)
Wu, Xin
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Srinuan, Chalita
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Rojniruttikul, Nuttawut
This study investigates the impact of human resource (HR) training on supply chain efficiency (SCE) among enterprises in Guiyang, China. A quantitative approach was used to analyse data from 316 respondents across ten key industrial sectors. Structural equation modelling (SEM) was applied to analyse the relationships between HR training, organisational culture, technological adaptability, and supply chain efficiency. The measurement model demonstrated adequate convergent validity (average variance extracted > 0.50) and internal consistency (composite reliability > 0.70). Discriminant validity was confirmed through the Fornell-Larcker criterion and HTMT ratio (< 0.85). The structural model revealed a positive influence of HR training on organisational culture (β = 0.664, p < 0.01), technological adaptability (β = 0.399, p < 0.01), and SCE made directly (β = 0.262, p < 0.05) and indirectly through organisational culture and technological adaptability (β = 0.272, p < 0.05). The model fit indices (χ<sup>2</sup>/df = 1.295; CFI = 0.984; TLI = 0.976; RMSEA = 0.031) confirmed its robustness. The findings suggest that HR training enhances SCE by improving employee skills, fostering technological integration, and cultivating an adaptive organisational culture. This research contributes to the theoretical understanding of HR development in supply chain management. It provides policymakers and business leaders with practical insights on leveraging workforce training as a strategic tool to enhance supply chain performance in regions such as Guiyang.
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AI-powered mixed reality acceptance in mining: A PLS-SEM and Bayesian Network modeling
(2025-12-01)
Yudhistyra, Wecka Imam
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Srinuan, Chalita
Facilitating digital transformation and sustainable management in the mining industry requires a strategic understanding of how emerging technologies are perceived and adopted by the workforce. Given the sector's traditionally conservative culture and its resistance to change, there remains a pressing need for empirical investigations that illuminate the pathways toward successful innovation adoption. This study explores the acceptance of AI-powered Mixed Reality (AIPMR) technology among the mining workforce in Indonesia, focusing on its potential to revolutionize human-machine interaction and contribute to smart mining solutions. Drawing upon the Technology Acceptance Model (TAM), an extended conceptual framework was developed to examine the influence of six key factors on employees’ intentions to adopt AIPMR technologies. Data were collected from 304 mining employees and analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM), further complemented by Bayesian Network analysis to enhance predictive robustness and uncover probabilistic interdependencies. The empirical results demonstrate that perceived usefulness, perceived ease of use, perceived novelty, top management support, and corporate culture significantly influence employees' attitudes toward adopting AIPMR technology, which subsequently impacts their acceptance of this innovation. The model in this research accounts for 72.6 % of the variance in intention to adopt AIPMR technology innovation. This research contributes to the literature by offering a data-driven foundation for developing decision support systems that align with the socio-technical dynamics of the mining industry. It also provides actionable insights for stakeholders seeking to implement technology acceptance strategies that facilitate sustainable digital transformation through the integration of AI-powered Mixed Reality in high-risk industrial environments.
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Adoption of industry-oriented enterprise resource planning systems: A rigorous empirical research in the mining industry leveraging PLS-SEM and artificial neural networks models
(2025-12-01)
Yudhistyra, Wecka Imam
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Srinuan, Chalita
Enhancing operational efficiency and enabling digital transformation in the mining industry can be effectively pursued through the implementation of Enterprise Resource Planning (ERP) systems. However, there remains a significant lack of empirical research to guide successful ERP adoption within the underexplored mining sector in developing countries, contexts often characterized by harsh operational conditions, cultural resistance to change, and limited innovation. This research aims to raise awareness and facilitate effective ERP adoption by identifying, analyzing, and critically assessing the key determinants influencing ERP system uptake in the mining industry. Data were collected from 278 employees across mining organizations in Indonesia and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), complemented by Artificial Neural Network (ANN) models to improve predictive accuracy and uncover both linear and non-linear relationships. The empirical findings reveal that employee perceptions, attitudes, and organizational factors significantly shape ERP adoption behaviors. Notably, organizational size emerged as the most influential predictor, surpassing even employee attitudes and perceptions, highlighting that institutional readiness could play a more decisive role than individual user disposition. This research contributes to academic literature by offering a data-driven framework tailored to the mining industry's operational and cultural dynamics in developing economies. Moreover, it provides actionable insights for policymakers, IT leaders, and practitioners seeking to design effective technology adoption strategies that support sustainable digital transformation through ERP integration.
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Exploring the Impact of Digital Platform on Energy-Efficient Consumption Behavior: A Multi-Group Analysis of Air Conditioning Purchase in China Using the Extended TPB Model
(2025-06-01)
Zheng, Zhong
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Srinuan, Chalita
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Rojniruttikul, Nuttawut
Energy-efficient consumption has become a strategic priority to mitigate global climate change and enhance national energy security. While social media has reshaped online consumption behavior, the mechanisms through which these digital platforms influence energy-efficient purchasing remain underexplored. This study extends the Theory of Planned Behavior (TPB) by integrating price perception variables and applies multi-group structural equation modeling to examine how social media shapes Chinese consumers’ intentions to purchase energy-efficient air conditioning. The results show that (1) social media exposure strengthens energy-efficient purchasing intentions indirectly via behavioral attitude, subjective norm, and perceived behavioral control; (2) price perception is negatively associated with purchase intention; and (3) these effects vary by age cohort, gender, and income—Generation Z and female consumers are more susceptible to social media influence, while low-income groups exhibit heightened price sensitivity. These findings advance TPB theory and offer guidance for digital platform policies aimed at promoting energy-efficient consumption.
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Influence of Social Media on Consumers' Green Purchase Intention of Air Conditioners: The Mediating Role of Behavioral Attitude
(2025-01-01)
Zheng, Zhong
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Srinuan, Chalita
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Rojniruttikul, Nuttawut
This study explores the factors influencing green purchase intentions among Chinese consumers in the context of air conditioners, focusing on the roles of behavioral attitudes and social media. The research examines three key hypotheses: the relationship between favorable behavioral attitudes and green purchase intentions, the influence of social media on shaping these attitudes, and the mediating role of social media in enhancing green purchase intentions. The findings support all three hypotheses, demonstrating a significant positive relationship between behavioral attitudes and green purchase intentions, a strong influence of social media on shaping favorable attitudes, and the mediating effect of social media in amplifying these attitudes' impact on purchase intentions. These results underscore the importance of leveraging social media for effective marketing strategies and fostering positive behavioral attitudes to promote environmentally friendly products. The study offers practical implications for businesses and policymakers aiming to enhance consumer engagement and drive green purchases. Future research directions include longitudinal studies, cross-cultural comparisons, and investigations into emerging technologies' impact on consumer behavior. This research contributes to the growing body of knowledge on digital consumer behavior and provides actionable insights for optimizing marketing strategies in the digital age.
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Enhancing Supply Chain Efficiency through HR Training: The Role of Technological Adaptability as a Mediator
(2025-01-01)
Wu, Xin
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Srinuan, Chalita
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Rojniruttikul, Nuttawut
In today's rapidly evolving business landscape, the interplay between human resource (HR) training, technological adaptability, and supply chain efficiency is paramount for organizational success. This study investigates these relationships by comprehensively analyzing existing literature and empirical data. Drawing on a documentary review methodology, the study synthesizes findings from academic journals, books, and conference proceedings to examine the impact of HR training on technological adaptability and supply chain efficiency. The results reveal significant positive correlations between HR training, technological adaptability, and supply chain efficiency, highlighting the critical role of HR training programs in fostering a technologically adaptable workforce and enhancing supply chain performance. Furthermore, mediation analysis suggests that technological adaptability partially mediates the relationship between HR training and supply chain efficiency, emphasizing the interconnectedness of these variables. The implications of these findings for policy formulation, educational enhancement, and entrepreneurial development are discussed, along with practical applications for business professionals and policymakers to enhance supply chain efficiency. Future research directions are also provided to advance our understanding of HR training's influence on supply chain dynamics in today's digital age.
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Exploring the acceptance of mixed reality technology innovation among mining industry workers
(2024-11-01)
Yudhistyra, Wecka Imam
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Srinuan, Chalita
Mixed Reality (MR) technology is an innovative technology that has the capability to transform and boost productivity in the mining industry. At the same time, the scarcity of guidelines and research in examining the acceptance of MR technology innovation in the mining sector could make the adoption of MR technology innovation unpredictable. Therefore, in order to support the successful adoption of MR technology innovation in the mining sector, this manuscript is designed to better understand the factors influencing its acceptance among workers. Based on the literature review, five factors are hypothesized to influence the acceptance of mining industry workers regarding MR technology innovation. The factors underwent testing on a sample of 253 mining industry workers, which was obtained via an office-intercept survey and processed using SmartPLS software. The model has proven to be highly robust, as indicated by its outstanding scores in validity, reliability, multicollinearity, and goodness of fit. The results from the analysis indicate that attitude, perceived usefulness, and perceived compatibility are significant factors influencing the acceptance of MR technology innovation. While perceived novelty is an important factor affecting attitude, it does not significantly impact the acceptance of MR technology innovation. Moreover, perceived ease of use was not found to be a significant factor in influencing the attitude and intention to adopt MR technology innovation. This research offers both theoretical and managerial implications for its findings.
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Analysis of Thailand's fixed broadband internet services demand: A merger screening of AIS and 3BB
(2024-03-01)
Phansatarn, Thunwar
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Srinuan, Chalita
The article conducted a ‘merger screening’ of the potential impact of the merger between AIS and 3BB, which were the second and third largest internet service providers among the four major operators prior to the merger in 2022. National survey data across country in 2022 and discrete choice model was used for the analysis. Surprisingly, finding showed that AIS and 3BB were not the closest competitors, but instead True, the provider with the largest market share, was their closest competitor. Based on the pre-merger analysis, it was predicted that service prices were likely to increase, and this merger is comprised a relevant market. To accurately define the market (market definition), other operators' services should not be considered as well. Considering the rising magnitude in service prices, it is estimated that, on average, prices will increase by 7.51%–31.51% compared to the current rates in 2022. Moreover, the study revealed that the merger would lead to an average increase in cost efficiency of 15.01% of the current marginal cost, which would allow the post-merger service prices to remain unchanged if the cost savings are passed on to consumers. A key policy recommendation was for regulatory agencies to conduct further analysis of competition, including evaluating entry barriers, the response of non-merging competitors, and competition without regulated pricing. Additionally, the study examines the potential future impacts on social welfare.
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Exploring the Acceptance of Technological Innovation Among Employees in the Mining Industry: A Study on Generative Artificial Intelligence
(2024-01-01)
Yudhistyra, Wecka Imam
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Srinuan, Chalita
Generative Artificial Intelligence (GenAI) technology innovation holds promise for revolutionizing the mining industry. However, the sector's traditionally conservative stance toward innovation has led to limited research. Thus, this manuscript aims to address this gap by exploring factors influencing the acceptance of GenAI technology innovation among mining industry employees from a developing country perspective. Based on literature reviews seven factors were identified to influence employees' acceptance of GenAI technological innovation. These factors were examined in a data sample of 286 mining industry employees collected via an office-intercept survey and analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM) using the SmartPLS software. Through thorough data analysis, models have been constructed that exhibit robustness, evidenced by their strong validity, reliability, and excellent fit with the data. The findings reveal that attitude, perceived usefulness, perceived ease of use, compatibility, company size, industrial competitiveness, and top management support significantly influence the acceptance of GenAI technology innovation. Notably, attitude was identified as the most influential factor, significantly shaping the likelihood of adoption. Conversely, company size had a comparatively minor impact on acceptance, and the regulatory framework showed no significant effect on the adoption of GenAI technology innovation. These results have important theoretical and managerial implications. Theoretically, they provide a nuanced understanding of the key factors driving technology acceptance in the mining industry, challenging the traditional emphasis on regulatory frameworks. Managerially, they underscore the importance of focusing on factors when developing strategies to foster technological adoption.

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